Abstract
In this technical note, an online learning algorithm is developed to solve the linear quadratic tracking (LQT) problem for partially-unknown continuous-time systems. It is shown that the value function is quadratic in terms of the state of the system and the command generator. Based on this quadratic form, an LQT Bellman equation and an LQT algebraic Riccati equation (ARE) are derived to solve the LQT problem. The integral reinforcement learning technique is used to find the solution to the LQT ARE online and without requiring the knowledge of the system drift dynamics or the command generator dynamics. The convergence of the proposed online algorithm to the optimal control solution is verified. To show the efficiency of the proposed approach, a simulation example is provided.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
